When AI fights instead of humans
· Source: aijourn.com
In short
The article examines the hidden dangers of multi-agent AI systems in enterprise production, using real-world examples like a logistics firm that lost $2 million due to conflicting agent goals and stale data. It outlines common failure modes—state conflicts, goal misalignment, infinite loops, cascading failures—and offers practical design patterns such as priority hierarchies and circuit breakers to mitigate risks. The author argues that without proper governance and coordination, multi-agent initiatives risk costly failures regardless of model quality.
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In the issue of week 20, 2026: AI-Powered Automation and Control Towers Drive Supply Chain Transformation
Tags: multi-agent, logistics, governance, coordination, automation, risk